To establish dose response relationships for diseases caused by long term exposures to pollutants, it is vital to determine exposures of individuals or cohorts as functions of time. Most existing occupational exposure databases do not contain continuous records of historical exposures to airborne contaminants. These gaps in the historical record may be filled by using the knowledge-base that experts and professionals in the field possess. This research proposes to develop a Bayesian framework obtaining estimates of exposure histories for airborne particulates from limited historical measurements, using subjective expert judgment. The expert judgments will be informed by knowledge of historical plant conditions and work practices, and models describing process-dependent aerosol generation, ventilation, and worker activity patterns. The method will also incorporate knowledge about sampler performance, relationships between different types of measurements, uncertainties in measurements, and systematic biases. The result of the synthesis will be probability distributions of the exposure of task- groups of workers at each past time interval, in the form of an exposure matrix. This matrix can then be potentially used in epidemiological studies.